Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations50000
Missing cells150000
Missing cells (%)7.3%
Total size in memory15.6 MiB
Average record size in memory328.0 B

Variable types

Text8
Unsupported3
Numeric30

Alerts

form_factor has 50000 (100.0%) missing valuesMissing
nand_type has 50000 (100.0%) missing valuesMissing
workload_type has 50000 (100.0%) missing valuesMissing
host_read_cmds_per_power_cycle is highly skewed (γ1 = 115.9535458)Skewed
composite_temperature_c has unique valuesUnique
iops has unique valuesUnique
bandwidth_read_gbps has unique valuesUnique
percentage_used has unique valuesUnique
wear_level_avg has unique valuesUnique
form_factor is an unsupported type, check if it needs cleaning or further analysisUnsupported
nand_type is an unsupported type, check if it needs cleaning or further analysisUnsupported
workload_type is an unsupported type, check if it needs cleaning or further analysisUnsupported
host_read_commands has 974 (1.9%) zerosZeros
host_write_commands has 740 (1.5%) zerosZeros
controller_busy_time has 13495 (27.0%) zerosZeros
unsafe_shutdowns has 12231 (24.5%) zerosZeros
media_errors has 871 (1.7%) zerosZeros
bad_block_count_grown has 3652 (7.3%) zerosZeros
pcie_uncorrectable_errors has 13094 (26.2%) zerosZeros
throttling_events has 17718 (35.4%) zerosZeros
host_read_cmds_per_power_cycle has 974 (1.9%) zerosZeros

Reproduction

Analysis started2025-12-20 00:23:40.045982
Analysis finished2025-12-20 00:23:41.697165
Duration1.65 second
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct47926
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:42.013500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length16
Median length15
Mean length14.50602
Min length13

Characters and Unicode

Total characters725301
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45899 ?
Unique (%)91.8%

Sample

1st row2/7/2025 18:04
2nd row11/24/2025 9:12
3rd row10/4/2025 21:12
4th row7/5/2025 15:29
5th row7/3/2025 3:58
ValueCountFrequency (%)
8/3/2025152
 
0.2%
6/20/2025146
 
0.1%
11/2/2025146
 
0.1%
2/24/2026145
 
0.1%
3/6/2025143
 
0.1%
4/12/2025143
 
0.1%
2/11/2026142
 
0.1%
2/26/2026142
 
0.1%
5/8/2025141
 
0.1%
11/27/2025141
 
0.1%
Other values (1853)98559
98.6%
2025-12-19T17:23:42.519346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2159482
22.0%
/100000
13.8%
184515
11.7%
078079
10.8%
569441
9.6%
50000
 
6.9%
:50000
 
6.9%
330595
 
4.2%
426192
 
3.6%
624547
 
3.4%
Other values (3)52450
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)725301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2159482
22.0%
/100000
13.8%
184515
11.7%
078079
10.8%
569441
9.6%
50000
 
6.9%
:50000
 
6.9%
330595
 
4.2%
426192
 
3.6%
624547
 
3.4%
Other values (3)52450
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)725301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2159482
22.0%
/100000
13.8%
184515
11.7%
078079
10.8%
569441
9.6%
50000
 
6.9%
:50000
 
6.9%
330595
 
4.2%
426192
 
3.6%
624547
 
3.4%
Other values (3)52450
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)725301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2159482
22.0%
/100000
13.8%
184515
11.7%
078079
10.8%
569441
9.6%
50000
 
6.9%
:50000
 
6.9%
330595
 
4.2%
426192
 
3.6%
624547
 
3.4%
Other values (3)52450
 
7.2%

form_factor
Unsupported

Missing  Rejected  Unsupported 

Missing50000
Missing (%)100.0%
Memory size390.8 KiB

nand_type
Unsupported

Missing  Rejected  Unsupported 

Missing50000
Missing (%)100.0%
Memory size390.8 KiB
Distinct28502
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:42.933493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters300000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14278 ?
Unique (%)28.6%

Sample

1st rowNV6150
2nd rowPM1423
3rd rowMT5017
4th rowPM2930
5th rowMT5457
ValueCountFrequency (%)
mt33618
 
< 0.1%
nv12848
 
< 0.1%
mt45267
 
< 0.1%
nv58757
 
< 0.1%
mt61767
 
< 0.1%
mt09577
 
< 0.1%
pm09636
 
< 0.1%
xg33816
 
< 0.1%
pm46326
 
< 0.1%
mt12876
 
< 0.1%
Other values (28492)49932
99.9%
2025-12-19T17:23:43.619611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M24938
 
8.3%
020224
 
6.7%
320096
 
6.7%
220067
 
6.7%
920049
 
6.7%
420037
 
6.7%
619997
 
6.7%
719983
 
6.7%
519924
 
6.6%
819834
 
6.6%
Other values (7)94851
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M24938
 
8.3%
020224
 
6.7%
320096
 
6.7%
220067
 
6.7%
920049
 
6.7%
420037
 
6.7%
619997
 
6.7%
719983
 
6.7%
519924
 
6.6%
819834
 
6.6%
Other values (7)94851
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M24938
 
8.3%
020224
 
6.7%
320096
 
6.7%
220067
 
6.7%
920049
 
6.7%
420037
 
6.7%
619997
 
6.7%
719983
 
6.7%
519924
 
6.6%
819834
 
6.6%
Other values (7)94851
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M24938
 
8.3%
020224
 
6.7%
320096
 
6.7%
220067
 
6.7%
920049
 
6.7%
420037
 
6.7%
619997
 
6.7%
719983
 
6.7%
519924
 
6.6%
819834
 
6.6%
Other values (7)94851
31.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:43.801793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters400000
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF63939.1
2nd rowX55210.7
3rd rowX55210.7
4th rowF63939.1
5th rowX55210.7
ValueCountFrequency (%)
x55210.710191
20.4%
q82710.29990
20.0%
r19283.39973
19.9%
l93847.59971
19.9%
f63939.19875
19.8%
2025-12-19T17:23:44.092947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.50000
12.5%
349667
12.4%
240144
10.0%
140029
10.0%
939694
9.9%
530353
7.6%
730152
7.5%
829934
7.5%
020181
 
5.0%
X10191
 
2.5%
Other values (6)59655
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.50000
12.5%
349667
12.4%
240144
10.0%
140029
10.0%
939694
9.9%
530353
7.6%
730152
7.5%
829934
7.5%
020181
 
5.0%
X10191
 
2.5%
Other values (6)59655
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.50000
12.5%
349667
12.4%
240144
10.0%
140029
10.0%
939694
9.9%
530353
7.6%
730152
7.5%
829934
7.5%
020181
 
5.0%
X10191
 
2.5%
Other values (6)59655
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.50000
12.5%
349667
12.4%
240144
10.0%
140029
10.0%
939694
9.9%
530353
7.6%
730152
7.5%
829934
7.5%
020181
 
5.0%
X10191
 
2.5%
Other values (6)59655
14.9%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:44.274679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.99004
Min length3

Characters and Unicode

Total characters299502
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLenovo
2nd rowDell
3rd rowFujitsu
4th rowHPE
5th rowHPE
ValueCountFrequency (%)
hpe8434
16.9%
fujitsu8410
16.8%
inspur8378
16.8%
lenovo8288
16.6%
dell8261
16.5%
supermicro8229
16.5%
2025-12-19T17:23:44.599892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u33427
 
11.2%
r24836
 
8.3%
o24805
 
8.3%
e24778
 
8.3%
s16788
 
5.6%
n16666
 
5.6%
i16639
 
5.6%
p16607
 
5.5%
l16522
 
5.5%
P8434
 
2.8%
Other values (12)100000
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)299502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u33427
 
11.2%
r24836
 
8.3%
o24805
 
8.3%
e24778
 
8.3%
s16788
 
5.6%
n16666
 
5.6%
i16639
 
5.6%
p16607
 
5.5%
l16522
 
5.5%
P8434
 
2.8%
Other values (12)100000
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)299502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u33427
 
11.2%
r24836
 
8.3%
o24805
 
8.3%
e24778
 
8.3%
s16788
 
5.6%
n16666
 
5.6%
i16639
 
5.6%
p16607
 
5.5%
l16522
 
5.5%
P8434
 
2.8%
Other values (12)100000
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)299502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u33427
 
11.2%
r24836
 
8.3%
o24805
 
8.3%
e24778
 
8.3%
s16788
 
5.6%
n16666
 
5.6%
i16639
 
5.6%
p16607
 
5.5%
l16522
 
5.5%
P8434
 
2.8%
Other values (12)100000
33.4%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:44.771888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length28
Median length24
Mean length17.79602
Min length11

Characters and Unicode

Total characters889801
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLenovo R740
2nd rowDell ThinkSystem SR650
3rd rowFujitsu UCS C240
4th rowHPE NF5280M6
5th rowHPE UCS C240
ValueCountFrequency (%)
thinksystem16501
12.4%
sr65016501
12.4%
ucs12643
9.5%
c24012643
9.5%
hpe8434
 
6.3%
nf5280m68411
 
6.3%
fujitsu8410
 
6.3%
inspur8378
 
6.3%
lenovo8288
 
6.2%
r7408288
 
6.2%
Other values (4)24804
18.6%
2025-12-19T17:23:45.105280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83301
 
9.4%
S53874
 
6.1%
050000
 
5.6%
e41279
 
4.6%
n37324
 
4.2%
i37297
 
4.2%
u33427
 
3.8%
s33289
 
3.7%
t29068
 
3.3%
r28993
 
3.3%
Other values (31)461949
51.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)889801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
83301
 
9.4%
S53874
 
6.1%
050000
 
5.6%
e41279
 
4.6%
n37324
 
4.2%
i37297
 
4.2%
u33427
 
3.8%
s33289
 
3.7%
t29068
 
3.3%
r28993
 
3.3%
Other values (31)461949
51.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)889801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
83301
 
9.4%
S53874
 
6.1%
050000
 
5.6%
e41279
 
4.6%
n37324
 
4.2%
i37297
 
4.2%
u33427
 
3.8%
s33289
 
3.7%
t29068
 
3.3%
r28993
 
3.3%
Other values (31)461949
51.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)889801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
83301
 
9.4%
S53874
 
6.1%
050000
 
5.6%
e41279
 
4.6%
n37324
 
4.2%
i37297
 
4.2%
u33427
 
3.8%
s33289
 
3.7%
t29068
 
3.3%
r28993
 
3.3%
Other values (31)461949
51.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:45.236487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.00216
Min length3

Characters and Unicode

Total characters200108
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAMD
2nd rowAMD
3rd rowIntel
4th rowIntel
5th rowIntel
ValueCountFrequency (%)
intel25054
50.1%
amd24946
49.9%
2025-12-19T17:23:45.518926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I25054
12.5%
n25054
12.5%
t25054
12.5%
e25054
12.5%
l25054
12.5%
A24946
12.5%
M24946
12.5%
D24946
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)200108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I25054
12.5%
n25054
12.5%
t25054
12.5%
e25054
12.5%
l25054
12.5%
A24946
12.5%
M24946
12.5%
D24946
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)200108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I25054
12.5%
n25054
12.5%
t25054
12.5%
e25054
12.5%
l25054
12.5%
A24946
12.5%
M24946
12.5%
D24946
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)200108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I25054
12.5%
n25054
12.5%
t25054
12.5%
e25054
12.5%
l25054
12.5%
A24946
12.5%
M24946
12.5%
D24946
12.5%

nvme_capacity_tb
Real number (ℝ)

Distinct14077
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.28459237
Minimum0
Maximum449.8645469
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:45.680304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9
Q13.8
median9.523613797
Q330.7
95-th percentile128.6
Maximum449.8645469
Range449.8645469
Interquartile range (IQR)26.9

Descriptive statistics

Standard deviation51.68797924
Coefficient of variation (CV)1.827425284
Kurtosis12.26950533
Mean28.28459237
Median Absolute Deviation (MAD)5.776386203
Skewness3.440408322
Sum1414229.618
Variance2671.647198
MonotonicityNot monotonic
2025-12-19T17:23:45.839494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.86620
13.2%
1.96404
12.8%
15.36285
12.6%
7.66279
12.6%
30.75093
 
10.2%
60.52531
 
5.1%
256.71363
 
2.7%
128.61335
 
2.7%
022
 
< 0.1%
20.429339831
 
< 0.1%
Other values (14067)14067
28.1%
ValueCountFrequency (%)
022
< 0.1%
0.21030362181
 
< 0.1%
0.32849702441
 
< 0.1%
0.35687153881
 
< 0.1%
0.42392657091
 
< 0.1%
ValueCountFrequency (%)
449.86454691
< 0.1%
438.44389641
< 0.1%
425.77516941
< 0.1%
408.14063831
< 0.1%
399.08033311
< 0.1%

overprovisioning_ratio
Real number (ℝ)

Distinct49984
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1887104105
Minimum0.05
Maximum0.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:45.982642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.118422626
Q10.1571329517
median0.1867662495
Q30.2181681918
95-th percentile0.2663009185
Maximum0.35
Range0.3
Interquartile range (IQR)0.06103524

Descriptive statistics

Standard deviation0.04457980625
Coefficient of variation (CV)0.2362339531
Kurtosis-0.1903887263
Mean0.1887104105
Median Absolute Deviation (MAD)0.030463373
Skewness0.2002214529
Sum9435.520523
Variance0.001987359126
MonotonicityNot monotonic
2025-12-19T17:23:46.144036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.056
 
< 0.1%
0.353
 
< 0.1%
0.15597832
 
< 0.1%
0.176307532
 
< 0.1%
0.190299962
 
< 0.1%
0.1471890522
 
< 0.1%
0.1571324862
 
< 0.1%
0.192217162
 
< 0.1%
0.1687831222
 
< 0.1%
0.1325578892
 
< 0.1%
Other values (49974)49975
> 99.9%
ValueCountFrequency (%)
0.056
< 0.1%
0.0506559411
 
< 0.1%
0.0545392771
 
< 0.1%
0.0546283491
 
< 0.1%
0.0550391281
 
< 0.1%
ValueCountFrequency (%)
0.353
< 0.1%
0.3473377491
 
< 0.1%
0.3470510371
 
< 0.1%
0.3428701581
 
< 0.1%
0.3418060311
 
< 0.1%

composite_temperature_c
Real number (ℝ)

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.73274948
Minimum23.85982181
Maximum43.20188451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:46.315506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum23.85982181
5-th percentile29.54997308
Q131.89156798
median33.72870173
Q335.55487276
95-th percentile37.92949042
Maximum43.20188451
Range19.3420627
Interquartile range (IQR)3.663304775

Descriptive statistics

Standard deviation2.573199239
Coefficient of variation (CV)0.07628193012
Kurtosis-0.3165517548
Mean33.73274948
Median Absolute Deviation (MAD)1.83239548
Skewness0.008978932088
Sum1686637.474
Variance6.621354322
MonotonicityNot monotonic
2025-12-19T17:23:46.484806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.46656891
 
< 0.1%
35.02233561
 
< 0.1%
34.550448181
 
< 0.1%
30.30933741
 
< 0.1%
29.121584611
 
< 0.1%
30.992629011
 
< 0.1%
34.291806621
 
< 0.1%
34.059644231
 
< 0.1%
34.903407451
 
< 0.1%
38.194776391
 
< 0.1%
Other values (49990)49990
> 99.9%
ValueCountFrequency (%)
23.859821811
< 0.1%
24.144929271
< 0.1%
24.759781961
< 0.1%
24.950202991
< 0.1%
25.101618321
< 0.1%
ValueCountFrequency (%)
43.201884511
< 0.1%
43.057110341
< 0.1%
43.026032361
< 0.1%
42.885442511
< 0.1%
42.883356321
< 0.1%

data_units_read
Real number (ℝ)

Distinct48005
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1059999.953
Minimum4117
Maximum25343283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:46.656157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4117
5-th percentile23451.7
Q175202
median205674.5
Q3702611.25
95-th percentile5402247.35
Maximum25343283
Range25339166
Interquartile range (IQR)627409.25

Descriptive statistics

Standard deviation2518703.167
Coefficient of variation (CV)2.376135168
Kurtosis23.84974475
Mean1059999.953
Median Absolute Deviation (MAD)162661.5
Skewness4.468552831
Sum5.299999767 × 1010
Variance6.343865646 × 1012
MonotonicityNot monotonic
2025-12-19T17:23:46.827618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
378224
 
< 0.1%
1592774
 
< 0.1%
434904
 
< 0.1%
203624
 
< 0.1%
291544
 
< 0.1%
248864
 
< 0.1%
302844
 
< 0.1%
670573
 
< 0.1%
335303
 
< 0.1%
228223
 
< 0.1%
Other values (47995)49963
99.9%
ValueCountFrequency (%)
41171
< 0.1%
42671
< 0.1%
43821
< 0.1%
46681
< 0.1%
46701
< 0.1%
ValueCountFrequency (%)
253432831
< 0.1%
246125291
< 0.1%
245333971
< 0.1%
244457501
< 0.1%
243827701
< 0.1%

data_units_written
Real number (ℝ)

Distinct48997
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2026676.06
Minimum6388
Maximum49950321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:46.991039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6388
5-th percentile41114.3
Q1140944
median387194.5
Q31343524
95-th percentile10242763.45
Maximum49950321
Range49943933
Interquartile range (IQR)1202580

Descriptive statistics

Standard deviation4881913.87
Coefficient of variation (CV)2.408827916
Kurtosis25.24389261
Mean2026676.06
Median Absolute Deviation (MAD)308134
Skewness4.581784392
Sum1.01333803 × 1011
Variance2.383308304 × 1013
MonotonicityNot monotonic
2025-12-19T17:23:47.152550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2818543
 
< 0.1%
182753
 
< 0.1%
960663
 
< 0.1%
418463
 
< 0.1%
492663
 
< 0.1%
1052283
 
< 0.1%
1585293
 
< 0.1%
790353
 
< 0.1%
603733
 
< 0.1%
546563
 
< 0.1%
Other values (48987)49970
99.9%
ValueCountFrequency (%)
63881
< 0.1%
64171
< 0.1%
66001
< 0.1%
67451
< 0.1%
67561
< 0.1%
ValueCountFrequency (%)
499503211
< 0.1%
496921641
< 0.1%
494613791
< 0.1%
491166711
< 0.1%
489940121
< 0.1%

host_read_commands
Real number (ℝ)

Zeros 

Distinct49015
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161988186.4
Minimum0
Maximum2147478246
Zeros974
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:47.323951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4046222.15
Q114679028
median40007590
Q3130331281.5
95-th percentile836913099.7
Maximum2147478246
Range2147478246
Interquartile range (IQR)115652253.5

Descriptive statistics

Standard deviation321767914.6
Coefficient of variation (CV)1.986366548
Kurtosis12.96074789
Mean161988186.4
Median Absolute Deviation (MAD)31733552.5
Skewness3.439719792
Sum8.09940932 × 1012
Variance1.035345909 × 1017
MonotonicityNot monotonic
2025-12-19T17:23:47.495483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0974
 
1.9%
85172792
 
< 0.1%
204577432
 
< 0.1%
310029062
 
< 0.1%
263010842
 
< 0.1%
161081252
 
< 0.1%
282240392
 
< 0.1%
121977222
 
< 0.1%
56809762
 
< 0.1%
91312152
 
< 0.1%
Other values (49005)49008
98.0%
ValueCountFrequency (%)
0974
1.9%
9973661
 
< 0.1%
10393581
 
< 0.1%
10812841
 
< 0.1%
10932601
 
< 0.1%
ValueCountFrequency (%)
21474782461
< 0.1%
21460989991
< 0.1%
21450040941
< 0.1%
21448558341
< 0.1%
21446612281
< 0.1%

host_write_commands
Real number (ℝ)

Zeros 

Distinct49255
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151058535.4
Minimum0
Maximum2146621866
Zeros740
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:47.654810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3730895.15
Q113079324.25
median35312992
Q3118861103.2
95-th percentile786722255.5
Maximum2146621866
Range2146621866
Interquartile range (IQR)105781779

Descriptive statistics

Standard deviation308337369
Coefficient of variation (CV)2.041178065
Kurtosis14.09143824
Mean151058535.4
Median Absolute Deviation (MAD)27893190
Skewness3.563338645
Sum7.55292677 × 1012
Variance9.50719331 × 1016
MonotonicityNot monotonic
2025-12-19T17:23:47.816087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0740
 
1.5%
65085072
 
< 0.1%
173412532
 
< 0.1%
86031612
 
< 0.1%
80308412
 
< 0.1%
57521282
 
< 0.1%
43962212
 
< 0.1%
972157421
 
< 0.1%
356363201
 
< 0.1%
151680981
 
< 0.1%
Other values (49245)49245
98.5%
ValueCountFrequency (%)
0740
1.5%
8252491
 
< 0.1%
8622551
 
< 0.1%
8765641
 
< 0.1%
8852601
 
< 0.1%
ValueCountFrequency (%)
21466218661
< 0.1%
21460085901
< 0.1%
21454941291
< 0.1%
21435955691
< 0.1%
21413078041
< 0.1%

avg_queue_depth
Real number (ℝ)

Distinct49797
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.38117936
Minimum1
Maximum73.44232982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:47.969357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.741514839
Q19.589051692
median21.10851359
Q330.86161035
95-th percentile42.87769477
Maximum73.44232982
Range72.44232982
Interquartile range (IQR)21.27255866

Descriptive statistics

Standard deviation12.56066826
Coefficient of variation (CV)0.5874637715
Kurtosis-0.7522852293
Mean21.38117936
Median Absolute Deviation (MAD)10.85256978
Skewness0.375970354
Sum1069058.968
Variance157.7703872
MonotonicityNot monotonic
2025-12-19T17:23:48.272165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1204
 
0.4%
42.345810551
 
< 0.1%
20.770060241
 
< 0.1%
7.7884314581
 
< 0.1%
10.861422941
 
< 0.1%
10.47190921
 
< 0.1%
16.128113091
 
< 0.1%
34.222316341
 
< 0.1%
8.2443635941
 
< 0.1%
6.2017857611
 
< 0.1%
Other values (49787)49787
99.6%
ValueCountFrequency (%)
1204
0.4%
1.0142152291
 
< 0.1%
1.017096961
 
< 0.1%
1.0268008571
 
< 0.1%
1.0296222131
 
< 0.1%
ValueCountFrequency (%)
73.442329821
< 0.1%
70.795740781
< 0.1%
67.464311321
< 0.1%
67.063799851
< 0.1%
65.27207171
< 0.1%

iops
Real number (ℝ)

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91762.02498
Minimum2278.55651
Maximum295292.9143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:48.444094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2278.55651
5-th percentile15999.21999
Q134075.80714
median99018.81397
Q3134745.1865
95-th percentile178283.5415
Maximum295292.9143
Range293014.3578
Interquartile range (IQR)100669.3793

Descriptive statistics

Standard deviation55132.08442
Coefficient of variation (CV)0.6008159087
Kurtosis-1.077907416
Mean91762.02498
Median Absolute Deviation (MAD)49878.10153
Skewness0.1170791391
Sum4588101249
Variance3039546733
MonotonicityNot monotonic
2025-12-19T17:23:48.605508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131570.49621
 
< 0.1%
120812.82171
 
< 0.1%
31564.11371
 
< 0.1%
29588.251181
 
< 0.1%
119679.73451
 
< 0.1%
31486.283661
 
< 0.1%
103431.23361
 
< 0.1%
125107.48931
 
< 0.1%
55642.238641
 
< 0.1%
92921.919931
 
< 0.1%
Other values (49990)49990
> 99.9%
ValueCountFrequency (%)
2278.556511
< 0.1%
2293.963771
< 0.1%
2496.9160711
< 0.1%
2523.8179681
< 0.1%
2557.0829611
< 0.1%
ValueCountFrequency (%)
295292.91431
< 0.1%
275070.58141
< 0.1%
262912.43691
< 0.1%
261785.00431
< 0.1%
260013.27661
< 0.1%

bandwidth_read_gbps
Real number (ℝ)

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.854474653
Minimum1.55 × 10-5
Maximum18.094694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:48.774937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.55 × 10-5
5-th percentile0.021407537
Q10.21751842
median1.142900154
Q32.627372772
95-th percentile6.337479559
Maximum18.094694
Range18.0946785
Interquartile range (IQR)2.409854351

Descriptive statistics

Standard deviation2.139598036
Coefficient of variation (CV)1.15374887
Kurtosis3.880636433
Mean1.854474653
Median Absolute Deviation (MAD)1.020885891
Skewness1.807820768
Sum92723.73267
Variance4.577879756
MonotonicityNot monotonic
2025-12-19T17:23:48.938363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.9078851811
 
< 0.1%
1.9056386041
 
< 0.1%
0.0832357931
 
< 0.1%
7.6812494521
 
< 0.1%
2.3166612441
 
< 0.1%
1.8495517521
 
< 0.1%
1.0365223791
 
< 0.1%
0.5215341221
 
< 0.1%
0.4056457941
 
< 0.1%
3.5776566741
 
< 0.1%
Other values (49990)49990
> 99.9%
ValueCountFrequency (%)
1.55 × 10-51
< 0.1%
2.16 × 10-51
< 0.1%
2.22 × 10-51
< 0.1%
2.49 × 10-51
< 0.1%
3.53 × 10-51
< 0.1%
ValueCountFrequency (%)
18.0946941
< 0.1%
17.329672821
< 0.1%
16.839559051
< 0.1%
16.797970891
< 0.1%
16.002187811
< 0.1%

bandwidth_write_gbps
Real number (ℝ)

Distinct49999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.490068452
Minimum2.21 × 10-6
Maximum16.99319337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:49.109883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.21 × 10-6
5-th percentile0.02459134735
Q10.228620651
median0.8610987735
Q32.001804331
95-th percentile5.204297993
Maximum16.99319337
Range16.99319116
Interquartile range (IQR)1.77318368

Descriptive statistics

Standard deviation1.872905117
Coefficient of variation (CV)1.256925556
Kurtosis8.289607735
Mean1.490068452
Median Absolute Deviation (MAD)0.7402010595
Skewness2.492104977
Sum74503.42259
Variance3.507773576
MonotonicityNot monotonic
2025-12-19T17:23:49.279963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3715976312
 
< 0.1%
4.3191072491
 
< 0.1%
1.0311313911
 
< 0.1%
4.1124671121
 
< 0.1%
0.5560563281
 
< 0.1%
1.0356161191
 
< 0.1%
0.1408020371
 
< 0.1%
0.4930506091
 
< 0.1%
0.0207127941
 
< 0.1%
0.4253269241
 
< 0.1%
Other values (49989)49989
> 99.9%
ValueCountFrequency (%)
2.21 × 10-61
< 0.1%
3.14 × 10-51
< 0.1%
3.78 × 10-51
< 0.1%
3.84 × 10-51
< 0.1%
4.61 × 10-51
< 0.1%
ValueCountFrequency (%)
16.993193371
< 0.1%
16.917442121
< 0.1%
16.549723661
< 0.1%
16.136413731
< 0.1%
16.000470761
< 0.1%

io_completion_time_ms
Real number (ℝ)

Distinct24680
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.719729053
Minimum3.374364835
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:49.443188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3.374364835
5-th percentile5.609682174
Q17.420488895
median10
Q310
95-th percentile10
Maximum10
Range6.625635165
Interquartile range (IQR)2.579511105

Descriptive statistics

Standard deviation1.609773585
Coefficient of variation (CV)0.1846127987
Kurtosis-0.5698756841
Mean8.719729053
Median Absolute Deviation (MAD)0
Skewness-0.8905967815
Sum435986.4527
Variance2.591370994
MonotonicityNot monotonic
2025-12-19T17:23:49.614722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025320
50.6%
7.5010177252
 
< 0.1%
8.7943320891
 
< 0.1%
8.6465850691
 
< 0.1%
7.730901021
 
< 0.1%
5.8559688171
 
< 0.1%
5.0696878731
 
< 0.1%
7.3660224541
 
< 0.1%
5.5270030011
 
< 0.1%
7.4865817291
 
< 0.1%
Other values (24670)24670
49.3%
ValueCountFrequency (%)
3.3743648351
< 0.1%
3.5721798731
< 0.1%
3.7688897181
< 0.1%
3.7929479271
< 0.1%
3.7984620841
< 0.1%
ValueCountFrequency (%)
1025320
50.6%
9.9994481641
 
< 0.1%
9.9992732371
 
< 0.1%
9.9985727211
 
< 0.1%
9.9985607261
 
< 0.1%

power_cycles
Real number (ℝ)

Distinct965
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.77112
Minimum1
Maximum1132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:49.776104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile84
Q1148
median235
Q3500
95-th percentile692
Maximum1132
Range1131
Interquartile range (IQR)352

Descriptive statistics

Standard deviation207.880147
Coefficient of variation (CV)0.6420589553
Kurtosis-0.8437627165
Mean323.77112
Median Absolute Deviation (MAD)131
Skewness0.5910302313
Sum16188556
Variance43214.15554
MonotonicityNot monotonic
2025-12-19T17:23:49.967197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133243
 
0.5%
151223
 
0.4%
168223
 
0.4%
145221
 
0.4%
153221
 
0.4%
172215
 
0.4%
138212
 
0.4%
146212
 
0.4%
155211
 
0.4%
165210
 
0.4%
Other values (955)47809
95.6%
ValueCountFrequency (%)
147
0.1%
22
 
< 0.1%
33
 
< 0.1%
44
 
< 0.1%
53
 
< 0.1%
ValueCountFrequency (%)
11321
< 0.1%
10521
< 0.1%
10481
< 0.1%
10271
< 0.1%
10241
< 0.1%

power_on_hours
Real number (ℝ)

Distinct49916
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13983.91378
Minimum5
Maximum43365.14623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:50.138557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile4199.951028
Q17927.793433
median12455.61686
Q319954.79229
95-th percentile26400.89203
Maximum43365.14623
Range43360.14623
Interquartile range (IQR)12026.99886

Descriptive statistics

Standard deviation7262.628445
Coefficient of variation (CV)0.5193559227
Kurtosis-0.8485635519
Mean13983.91378
Median Absolute Deviation (MAD)5619.427992
Skewness0.3838646422
Sum699195689
Variance52745771.93
MonotonicityNot monotonic
2025-12-19T17:23:50.299768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
585
 
0.2%
8414.0757521
 
< 0.1%
20300.996081
 
< 0.1%
20549.62981
 
< 0.1%
10307.460761
 
< 0.1%
4512.095391
 
< 0.1%
7493.9129161
 
< 0.1%
2831.6214261
 
< 0.1%
9076.3451621
 
< 0.1%
23302.938221
 
< 0.1%
Other values (49906)49906
99.8%
ValueCountFrequency (%)
585
0.2%
7.9680671871
 
< 0.1%
35.101803871
 
< 0.1%
44.315667731
 
< 0.1%
51.855604231
 
< 0.1%
ValueCountFrequency (%)
43365.146231
< 0.1%
40377.628751
< 0.1%
39794.68631
< 0.1%
38779.851411
< 0.1%
38639.029351
< 0.1%

controller_busy_time
Real number (ℝ)

Zeros 

Distinct11159
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3523.4195
Minimum0
Maximum82983
Zeros13495
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:50.461107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median862
Q32496.25
95-th percentile18837.4
Maximum82983
Range82983
Interquartile range (IQR)2496.25

Descriptive statistics

Standard deviation7997.081515
Coefficient of variation (CV)2.269693267
Kurtosis19.28485643
Mean3523.4195
Median Absolute Deviation (MAD)862
Skewness4.060160333
Sum176170975
Variance63953312.76
MonotonicityNot monotonic
2025-12-19T17:23:50.622525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013495
 
27.0%
35929
 
0.1%
26625
 
0.1%
14425
 
0.1%
27324
 
< 0.1%
75324
 
< 0.1%
16723
 
< 0.1%
112922
 
< 0.1%
85922
 
< 0.1%
88622
 
< 0.1%
Other values (11149)36289
72.6%
ValueCountFrequency (%)
013495
27.0%
112
 
< 0.1%
213
 
< 0.1%
312
 
< 0.1%
417
 
< 0.1%
ValueCountFrequency (%)
829831
< 0.1%
806001
< 0.1%
804081
< 0.1%
798911
< 0.1%
786351
< 0.1%

percentage_used
Real number (ℝ)

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.34549148
Minimum5.309041032
Maximum96.7458477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:50.782306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5.309041032
5-th percentile12.06505816
Q129.49653478
median44.6637099
Q360.30401739
95-th percentile86.90604853
Maximum96.7458477
Range91.43680667
Interquartile range (IQR)30.80748262

Descriptive statistics

Standard deviation22.01384928
Coefficient of variation (CV)0.4749944078
Kurtosis-0.7003887045
Mean46.34549148
Median Absolute Deviation (MAD)15.40348539
Skewness0.2891119191
Sum2317274.574
Variance484.6095603
MonotonicityNot monotonic
2025-12-19T17:23:50.945741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.096114141
 
< 0.1%
15.442363841
 
< 0.1%
20.187707621
 
< 0.1%
40.0586331
 
< 0.1%
54.378529951
 
< 0.1%
7.5066258751
 
< 0.1%
65.13170191
 
< 0.1%
12.035249031
 
< 0.1%
41.97049521
 
< 0.1%
55.31995451
 
< 0.1%
Other values (49990)49990
> 99.9%
ValueCountFrequency (%)
5.3090410321
< 0.1%
5.4403111411
< 0.1%
5.4838981361
< 0.1%
5.4936673481
< 0.1%
5.5777602751
< 0.1%
ValueCountFrequency (%)
96.74584771
< 0.1%
96.487870891
< 0.1%
96.462777241
< 0.1%
95.879561041
< 0.1%
95.84835931
< 0.1%

wear_level_avg
Real number (ℝ)

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.80928251
Minimum5.576200852
Maximum166.9212612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:51.107080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5.576200852
5-th percentile23.66594083
Q142.38838481
median58.52035472
Q376.98839763
95-th percentile105.6736899
Maximum166.9212612
Range161.3450603
Interquartile range (IQR)34.60001283

Descriptive statistics

Standard deviation24.90938026
Coefficient of variation (CV)0.4096312147
Kurtosis-0.06078029197
Mean60.80928251
Median Absolute Deviation (MAD)17.14208022
Skewness0.4693258941
Sum3040464.125
Variance620.4772248
MonotonicityNot monotonic
2025-12-19T17:23:51.276398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.35053261
 
< 0.1%
49.104109091
 
< 0.1%
16.787973491
 
< 0.1%
48.388875931
 
< 0.1%
56.003155131
 
< 0.1%
15.172853141
 
< 0.1%
86.598875441
 
< 0.1%
31.626724451
 
< 0.1%
45.088862771
 
< 0.1%
88.749334151
 
< 0.1%
Other values (49990)49990
> 99.9%
ValueCountFrequency (%)
5.5762008521
< 0.1%
6.0320910431
< 0.1%
6.1776700781
< 0.1%
6.5892375931
< 0.1%
6.6189181481
< 0.1%
ValueCountFrequency (%)
166.92126121
< 0.1%
163.81895931
< 0.1%
162.90398171
< 0.1%
162.52019551
< 0.1%
161.45309951
< 0.1%

wear_level_max
Real number (ℝ)

Distinct49999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.98534388
Minimum6.249380234
Maximum218.0970997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:51.447750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6.249380234
5-th percentile28.13440669
Q150.4575915
median70.04498772
Q392.27603025
95-th percentile128.5433012
Maximum218.0970997
Range211.8477195
Interquartile range (IQR)41.81843875

Descriptive statistics

Standard deviation30.42518148
Coefficient of variation (CV)0.4168670018
Kurtosis0.04909339166
Mean72.98534388
Median Absolute Deviation (MAD)20.76139087
Skewness0.5205239467
Sum3649267.194
Variance925.6916682
MonotonicityNot monotonic
2025-12-19T17:23:51.609050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.953641792
 
< 0.1%
109.9390081
 
< 0.1%
27.385309351
 
< 0.1%
56.513423951
 
< 0.1%
67.630227491
 
< 0.1%
17.471886091
 
< 0.1%
92.636212261
 
< 0.1%
39.45532681
 
< 0.1%
60.39515741
 
< 0.1%
114.42614161
 
< 0.1%
Other values (49989)49989
> 99.9%
ValueCountFrequency (%)
6.2493802341
< 0.1%
6.6469260621
< 0.1%
6.7549387911
< 0.1%
8.1543331821
< 0.1%
8.1802271571
< 0.1%
ValueCountFrequency (%)
218.09709971
< 0.1%
209.24307761
< 0.1%
206.73563611
< 0.1%
201.30417461
< 0.1%
200.95358161
< 0.1%
Distinct49784
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.63183367
Minimum0
Maximum100
Zeros115
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:51.904001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.90433826
Q138.66326651
median55.33309291
Q370.59413799
95-th percentile88.36117941
Maximum100
Range100
Interquartile range (IQR)31.93087148

Descriptive statistics

Standard deviation22.54146046
Coefficient of variation (CV)0.4203000144
Kurtosis-0.6424718303
Mean53.63183367
Median Absolute Deviation (MAD)15.91214696
Skewness-0.2656761969
Sum2681591.683
Variance508.1174398
MonotonicityNot monotonic
2025-12-19T17:23:52.065430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0115
 
0.2%
100102
 
0.2%
74.835745432
 
< 0.1%
27.097508981
 
< 0.1%
34.496590461
 
< 0.1%
30.999107071
 
< 0.1%
85.753675821
 
< 0.1%
16.986463351
 
< 0.1%
12.155163621
 
< 0.1%
70.283341471
 
< 0.1%
Other values (49774)49774
99.5%
ValueCountFrequency (%)
0115
0.2%
0.0115913741
 
< 0.1%
0.0305063841
 
< 0.1%
0.062271721
 
< 0.1%
0.0730971061
 
< 0.1%
ValueCountFrequency (%)
100102
0.2%
99.989003781
 
< 0.1%
99.98859241
 
< 0.1%
99.985536061
 
< 0.1%
99.969806921
 
< 0.1%

unsafe_shutdowns
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.79592
Minimum0
Maximum11
Zeros12231
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:52.205444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile5
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.681810906
Coefficient of variation (CV)0.9364620395
Kurtosis0.8264124644
Mean1.79592
Median Absolute Deviation (MAD)1
Skewness1.068308342
Sum89796
Variance2.828487923
MonotonicityNot monotonic
2025-12-19T17:23:52.336932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
114529
29.1%
012231
24.5%
29367
18.7%
35817
11.6%
43881
 
7.8%
52359
 
4.7%
61190
 
2.4%
7444
 
0.9%
8132
 
0.3%
942
 
0.1%
Other values (2)8
 
< 0.1%
ValueCountFrequency (%)
012231
24.5%
114529
29.1%
29367
18.7%
35817
11.6%
43881
 
7.8%
ValueCountFrequency (%)
111
 
< 0.1%
107
 
< 0.1%
942
 
0.1%
8132
 
0.3%
7444
0.9%

background_scrub_time_pct
Real number (ℝ)

Distinct49999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.038239429
Minimum0
Maximum8.633550272
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:52.490546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.22948314
Q13.28413084
median4.03033335
Q34.782083736
95-th percentile5.881316651
Maximum8.633550272
Range8.633550272
Interquartile range (IQR)1.497952896

Descriptive statistics

Standard deviation1.112131786
Coefficient of variation (CV)0.2754001602
Kurtosis0.008283971323
Mean4.038239429
Median Absolute Deviation (MAD)0.749087522
Skewness0.03067984527
Sum201911.9714
Variance1.236837109
MonotonicityNot monotonic
2025-12-19T17:23:52.653267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
< 0.1%
2.8301356831
 
< 0.1%
4.0909136341
 
< 0.1%
5.3354134251
 
< 0.1%
4.2955679331
 
< 0.1%
3.7223906261
 
< 0.1%
1.7210355781
 
< 0.1%
6.8699961091
 
< 0.1%
2.3884235471
 
< 0.1%
4.0633886471
 
< 0.1%
Other values (49989)49989
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
0.0564004641
< 0.1%
0.0725909151
< 0.1%
0.0822811321
< 0.1%
0.1191857041
< 0.1%
ValueCountFrequency (%)
8.6335502721
< 0.1%
8.5886710581
< 0.1%
8.5361798091
< 0.1%
8.444291891
< 0.1%
8.1790425861
< 0.1%

gc_active_time_pct
Real number (ℝ)

Distinct49785
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.581921946
Minimum0
Maximum40
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:52.806877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.529137076
Q15.179233562
median6.438012093
Q38.002658938
95-th percentile16.2269417
Maximum40
Range40
Interquartile range (IQR)2.823425377

Descriptive statistics

Standard deviation4.986301138
Coefficient of variation (CV)0.657656617
Kurtosis16.00046364
Mean7.581921946
Median Absolute Deviation (MAD)1.378159703
Skewness3.578451983
Sum379096.0973
Variance24.86319904
MonotonicityNot monotonic
2025-12-19T17:23:53.007368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40213
 
0.4%
04
 
< 0.1%
8.7451451691
 
< 0.1%
5.6828452491
 
< 0.1%
5.0851180521
 
< 0.1%
21.397990351
 
< 0.1%
4.5258778071
 
< 0.1%
6.8414277441
 
< 0.1%
4.433329431
 
< 0.1%
5.4710957691
 
< 0.1%
Other values (49775)49775
99.6%
ValueCountFrequency (%)
04
< 0.1%
0.013449481
 
< 0.1%
0.055746371
 
< 0.1%
0.0994984281
 
< 0.1%
0.2573702031
 
< 0.1%
ValueCountFrequency (%)
40213
0.4%
39.955779311
 
< 0.1%
39.937771551
 
< 0.1%
39.916320411
 
< 0.1%
39.892821831
 
< 0.1%

media_errors
Real number (ℝ)

Zeros 

Distinct120
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.99452
Minimum0
Maximum123
Zeros871
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:53.170086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q312
95-th percentile31
Maximum123
Range123
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.73370374
Coefficient of variation (CV)1.174013734
Kurtosis17.05966379
Mean9.99452
Median Absolute Deviation (MAD)4
Skewness3.56196067
Sum499726
Variance137.6798036
MonotonicityNot monotonic
2025-12-19T17:23:53.339312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44793
 
9.6%
34613
 
9.2%
54327
 
8.7%
23842
 
7.7%
63778
 
7.6%
73334
 
6.7%
82857
 
5.7%
12451
 
4.9%
92360
 
4.7%
102078
 
4.2%
Other values (110)15567
31.1%
ValueCountFrequency (%)
0871
 
1.7%
12451
4.9%
23842
7.7%
34613
9.2%
44793
9.6%
ValueCountFrequency (%)
1232
< 0.1%
1201
< 0.1%
1192
< 0.1%
1181
< 0.1%
1151
< 0.1%
Distinct195
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.62178
Minimum1
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:53.508539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median14
Q322
95-th percentile55
Maximum208
Range207
Interquartile range (IQR)13

Descriptive statistics

Standard deviation19.81043207
Coefficient of variation (CV)1.009614422
Kurtosis18.24264876
Mean19.62178
Median Absolute Deviation (MAD)5
Skewness3.736396358
Sum981089
Variance392.4532187
MonotonicityNot monotonic
2025-12-19T17:23:53.671258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93216
 
6.4%
103174
 
6.3%
82962
 
5.9%
112958
 
5.9%
122866
 
5.7%
132587
 
5.2%
72411
 
4.8%
142265
 
4.5%
152046
 
4.1%
61842
 
3.7%
Other values (185)23673
47.3%
ValueCountFrequency (%)
114
 
< 0.1%
2103
 
0.2%
3251
 
0.5%
4692
1.4%
51243
2.5%
ValueCountFrequency (%)
2081
< 0.1%
2051
< 0.1%
2041
< 0.1%
2031
< 0.1%
1991
< 0.1%

bad_block_count_grown
Real number (ℝ)

Zeros 

Distinct90
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.03216
Minimum0
Maximum92
Zeros3652
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:53.824864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile21
Maximum92
Range92
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.427826903
Coefficient of variation (CV)1.397149098
Kurtosis19.53998311
Mean6.03216
Median Absolute Deviation (MAD)2
Skewness3.858923967
Sum301608
Variance71.0282663
MonotonicityNot monotonic
2025-12-19T17:23:53.994090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27551
15.1%
16829
13.7%
36690
13.4%
45320
10.6%
54018
8.0%
03652
7.3%
62974
 
5.9%
72318
 
4.6%
81730
 
3.5%
91326
 
2.7%
Other values (80)7592
15.2%
ValueCountFrequency (%)
03652
7.3%
16829
13.7%
27551
15.1%
36690
13.4%
45320
10.6%
ValueCountFrequency (%)
922
< 0.1%
912
< 0.1%
881
< 0.1%
872
< 0.1%
851
< 0.1%

pcie_correctable_errors
Real number (ℝ)

Distinct102
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.38496
Minimum0
Maximum107
Zeros359
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:54.156812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median14
Q324
95-th percentile52
Maximum107
Range107
Interquartile range (IQR)16

Descriptive statistics

Standard deviation15.32801568
Coefficient of variation (CV)0.8337258108
Kurtosis2.544359618
Mean18.38496
Median Absolute Deviation (MAD)7
Skewness1.581378055
Sum919248
Variance234.9480648
MonotonicityNot monotonic
2025-12-19T17:23:54.326040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72144
 
4.3%
82138
 
4.3%
92090
 
4.2%
102085
 
4.2%
62079
 
4.2%
122058
 
4.1%
112049
 
4.1%
52040
 
4.1%
131843
 
3.7%
141789
 
3.6%
Other values (92)29685
59.4%
ValueCountFrequency (%)
0359
 
0.7%
1811
1.6%
21300
2.6%
31572
3.1%
41776
3.6%
ValueCountFrequency (%)
1071
 
< 0.1%
1001
 
< 0.1%
993
< 0.1%
982
< 0.1%
971
 
< 0.1%

pcie_uncorrectable_errors
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.762
Minimum0
Maximum14
Zeros13094
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:54.457519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.731362456
Coefficient of variation (CV)0.9826120633
Kurtosis2.298069887
Mean1.762
Median Absolute Deviation (MAD)1
Skewness1.346830425
Sum88100
Variance2.997615952
MonotonicityNot monotonic
2025-12-19T17:23:54.595504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
113853
27.7%
013094
26.2%
29791
19.6%
35954
11.9%
43425
 
6.9%
51921
 
3.8%
61011
 
2.0%
7502
 
1.0%
8249
 
0.5%
9123
 
0.2%
Other values (5)77
 
0.2%
ValueCountFrequency (%)
013094
26.2%
113853
27.7%
29791
19.6%
35954
11.9%
43425
 
6.9%
ValueCountFrequency (%)
142
 
< 0.1%
134
 
< 0.1%
126
 
< 0.1%
1117
 
< 0.1%
1048
0.1%

workload_type
Unsupported

Missing  Rejected  Unsupported 

Missing50000
Missing (%)100.0%
Memory size390.8 KiB

power_draw_w
Real number (ℝ)

Distinct44077
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.77911989
Minimum4.04298236
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:54.742604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4.04298236
5-th percentile7.611557714
Q19.170844636
median10.8214684
Q314.05600288
95-th percentile18
Maximum18
Range13.95701764
Interquartile range (IQR)4.885158244

Descriptive statistics

Standard deviation3.364091791
Coefficient of variation (CV)0.2855978904
Kurtosis-0.7635443614
Mean11.77911989
Median Absolute Deviation (MAD)2.035295003
Skewness0.6339610157
Sum588955.9945
Variance11.31711358
MonotonicityNot monotonic
2025-12-19T17:23:54.911831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185924
 
11.8%
11.133804671
 
< 0.1%
16.714530671
 
< 0.1%
9.2035234421
 
< 0.1%
8.5561413531
 
< 0.1%
8.9628441321
 
< 0.1%
7.600379561
 
< 0.1%
11.661452331
 
< 0.1%
5.5644907161
 
< 0.1%
16.717231711
 
< 0.1%
Other values (44067)44067
88.1%
ValueCountFrequency (%)
4.042982361
< 0.1%
4.5277096251
< 0.1%
4.6191592661
< 0.1%
4.6279587191
< 0.1%
4.6328238941
< 0.1%
ValueCountFrequency (%)
185924
11.8%
17.997442751
 
< 0.1%
17.997090551
 
< 0.1%
17.996938431
 
< 0.1%
17.996588541
 
< 0.1%

throttling_events
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.61404
Minimum0
Maximum14
Zeros17718
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:55.058931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.903446175
Coefficient of variation (CV)1.179305454
Kurtosis3.305185545
Mean1.61404
Median Absolute Deviation (MAD)1
Skewness1.66480483
Sum80702
Variance3.623107341
MonotonicityNot monotonic
2025-12-19T17:23:55.181293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
017718
35.4%
112774
25.5%
27571
15.1%
34764
 
9.5%
42857
 
5.7%
51767
 
3.5%
61112
 
2.2%
7674
 
1.3%
8370
 
0.7%
9194
 
0.4%
Other values (5)199
 
0.4%
ValueCountFrequency (%)
017718
35.4%
112774
25.5%
27571
15.1%
34764
 
9.5%
42857
 
5.7%
ValueCountFrequency (%)
146
 
< 0.1%
1313
 
< 0.1%
1220
 
< 0.1%
1142
 
0.1%
10118
0.2%
Distinct9940
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:55.473572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters600000
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique327 ?
Unique (%)0.7%

Sample

1st rowABCD2298EFGH
2nd rowABCD1738EFGH
3rd rowABCD6534EFGH
4th rowABCD8091EFGH
5th rowABCD6951EFGH
ValueCountFrequency (%)
abcd5913efgh17
 
< 0.1%
abcd4347efgh16
 
< 0.1%
abcd6307efgh15
 
< 0.1%
abcd6330efgh14
 
< 0.1%
abcd9976efgh13
 
< 0.1%
abcd3622efgh13
 
< 0.1%
abcd7942efgh13
 
< 0.1%
abcd6647efgh13
 
< 0.1%
abcd9494efgh13
 
< 0.1%
abcd3484efgh13
 
< 0.1%
Other values (9930)49860
99.7%
2025-12-19T17:23:55.900976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A50000
 
8.3%
E50000
 
8.3%
H50000
 
8.3%
G50000
 
8.3%
B50000
 
8.3%
F50000
 
8.3%
D50000
 
8.3%
C50000
 
8.3%
420132
 
3.4%
920104
 
3.4%
Other values (8)159764
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)600000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A50000
 
8.3%
E50000
 
8.3%
H50000
 
8.3%
G50000
 
8.3%
B50000
 
8.3%
F50000
 
8.3%
D50000
 
8.3%
C50000
 
8.3%
420132
 
3.4%
920104
 
3.4%
Other values (8)159764
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)600000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A50000
 
8.3%
E50000
 
8.3%
H50000
 
8.3%
G50000
 
8.3%
B50000
 
8.3%
F50000
 
8.3%
D50000
 
8.3%
C50000
 
8.3%
420132
 
3.4%
920104
 
3.4%
Other values (8)159764
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)600000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A50000
 
8.3%
E50000
 
8.3%
H50000
 
8.3%
G50000
 
8.3%
B50000
 
8.3%
F50000
 
8.3%
D50000
 
8.3%
C50000
 
8.3%
420132
 
3.4%
920104
 
3.4%
Other values (8)159764
26.6%
Distinct1000
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:56.224192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters450000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD3V612VAB
2nd rowD3V735VAB
3rd rowD3V082VAB
4th rowD3V761VAB
5th rowD3V451VAB
ValueCountFrequency (%)
d3v818vab76
 
0.2%
d3v641vab74
 
0.1%
d3v697vab73
 
0.1%
d3v065vab73
 
0.1%
d3v812vab72
 
0.1%
d3v804vab71
 
0.1%
d3v531vab70
 
0.1%
d3v947vab70
 
0.1%
d3v940vab69
 
0.1%
d3v616vab69
 
0.1%
Other values (990)49283
98.6%
2025-12-19T17:23:56.708677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
V100000
22.2%
365143
14.5%
D50000
11.1%
A50000
11.1%
B50000
11.1%
115221
 
3.4%
515080
 
3.4%
415049
 
3.3%
714999
 
3.3%
814970
 
3.3%
Other values (4)59538
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)450000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V100000
22.2%
365143
14.5%
D50000
11.1%
A50000
11.1%
B50000
11.1%
115221
 
3.4%
515080
 
3.4%
415049
 
3.3%
714999
 
3.3%
814970
 
3.3%
Other values (4)59538
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)450000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V100000
22.2%
365143
14.5%
D50000
11.1%
A50000
11.1%
B50000
11.1%
115221
 
3.4%
515080
 
3.4%
415049
 
3.3%
714999
 
3.3%
814970
 
3.3%
Other values (4)59538
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)450000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V100000
22.2%
365143
14.5%
D50000
11.1%
A50000
11.1%
B50000
11.1%
115221
 
3.4%
515080
 
3.4%
415049
 
3.3%
714999
 
3.3%
814970
 
3.3%
Other values (4)59538
13.2%

host_read_cmds_per_power_cycle
Real number (ℝ)

Skewed  Zeros 

Distinct49026
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean597256.549
Minimum0
Maximum1434988322
Zeros974
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-12-19T17:23:56.890037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19388.3073
Q164316.35271
median174522.8794
Q3510082.1351
95-th percentile2021263.959
Maximum1434988322
Range1434988322
Interquartile range (IQR)445765.7824

Descriptive statistics

Standard deviation9270178.054
Coefficient of variation (CV)15.52126648
Kurtosis15298.11165
Mean597256.549
Median Absolute Deviation (MAD)136608.4401
Skewness115.9535458
Sum2.986282745 × 1010
Variance8.593620115 × 1013
MonotonicityNot monotonic
2025-12-19T17:23:57.200902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0974
 
1.9%
2705142
 
< 0.1%
265903.01421
 
< 0.1%
79277.692551
 
< 0.1%
527412.79271
 
< 0.1%
294853.45651
 
< 0.1%
137741.59781
 
< 0.1%
142829.30541
 
< 0.1%
471622.27121
 
< 0.1%
72662.208451
 
< 0.1%
Other values (49016)49016
98.0%
ValueCountFrequency (%)
0974
1.9%
4355.3100441
 
< 0.1%
4900.9196791
 
< 0.1%
5404.1990951
 
< 0.1%
5521.5965521
 
< 0.1%
ValueCountFrequency (%)
14349883221
< 0.1%
9949383171
< 0.1%
7520770101
< 0.1%
5402858571
< 0.1%
3636008641
< 0.1%